Acoustic data optimisation for seabed mapping with visual and computational data mining

Oceans cover 70% of Earth’s surface but little is known about their waters. While the echosounders, often used for exploration of our oceans, have developed at a tremendous rate since the WWII, the methods used to analyse and interpret the data still remain the same. These methods are inefficient, time consuming, and often costly in dealing with the large data that modern echosounders produce. This PhD project will examine the complexity of the de facto seabed mapping technique by exploring and analysing acoustic data with a combination of data mining and visual analytic methods. First we test the redundancy issues in multibeam echosounder (MBES) data by using the component plane visualisation of a Self Organising Map (SOM). A total of 16 visual groups were identified among the 132 statistical data descriptors. The optimised MBES dataset had 35 attributes from 16 visual groups and represented a 73% reduction in data dimensionality. A combined Principal Component Analysis (PCA) + k-means was used to cluster both the datasets. The cluster results were visually compared as well as internally validated using four different internal validation methods. Next we tested two novel approaches in singlebeam echosounder (SBES) data processing and clustering – using visual exploration for outlier detection and direct clustering of time series echo returns. Visual exploration identified further outliers the automatic procedure was not able to find. The SBES data were then clustered directly. The internal validation indices suggested the optimal number of clusters to be three. This is consistent with the assumption that the SBES time series represented the subsurface classes of the seabed. Next the SBES data were joined with the corresponding MBES data based on identification of the closest locations between MBES and SBES. Two algorithms, PCA + k-means and fuzzy c-means were tested and results visualised. From visual comparison, the cluster boundary appeared to have better definitions when compared to the clustered MBES data only. The results seem to indicate that adding SBES did in fact improve the boundary definitions. Next the cluster results from the analysis chapters were validated against ground truth data using a confusion matrix and kappa coefficients. For MBES, the classes derived from optimised data yielded better accuracy compared to that of the original data. For SBES, direct clustering was able to provide a relatively reliable overview of the underlying classes in survey area. The combined MBES + SBES data provided by far the best accuracy for mapping with almost a 10% increase in overall accuracy compared to that of the original MBES data. The results proved to be promising in optimising the acoustic data and improving the quality of seabed mapping. Furthermore, these approaches have the potential of significant time and cost saving in the seabed mapping process. Finally some future directions are recommended for the findings of this research project with the consideration that this could contribute to further development of seabed mapping problems at mapping agencies worldwide

Spatial and Topological Analysis of Urban Land Cover Structure in New Orleans Using Multispectral Aerial Image and Lidar Data

Urban land use and land cover (LULC) mapping has been one of the major applications in remote sensing of the urban environment. Land cover refers to the biophysical materials at the surface of the earth (i.e. grass, trees, soils, concrete, water), while land use indicates the socio-economic function of the land (i.e., residential, industrial, commercial land uses). This study addresses the technical issue of how to computationally infer urban land use types based on the urban land cover structures from remote sensing data. In this research, a multispectral aerial image and high-resolution LiDAR topographic data have been integrated to investigate the urban land cover and land use in New Orleans, Louisiana. First, the LiDAR data are used to solve the problems associated with solar shadows of trees and buildings, building lean and occlusions in the multispectral aerial image. A two-stage rule-based classification approach has been developed, and the urban land cover of New Orleans has been classified into six categories: water, grass, trees, imperious ground, elevated bridges, and buildings with an overall classification accuracy of 94.2%, significantly higher than that of traditional per-pixel based classification method. The buildings are further classified into regular low-rising, multi-story, mid-rise, high-rise, and skyscrapers in terms of the height. Second, the land cover composition and structure in New Orleans have been quantitatively analyzed for the first time in terms of urban planning districts, and the information and knowledge about the characteristics of urban land cover components and structure for different types of land use functions have been discovered. Third, a graph-theoretic data model, known as relational attribute neighborhood graph (RANG), is adopted to comprehensively represent geometrical and thematic attributes, compositional and structural properties, spatial/topological relations between urban land cover patches (objects). Based on the evaluation of the importance of 26 spatial, thematic and topological variables in RANG, the random forest classification method is utilized to computationally infer and classify the urban land use in New Orleans into 7 types at the urban block level: single-family residential, two-family residential, multi-family residential, commercial, CBD, institutional, parks and open space, with an overall accuracy of 91.7%

Forest landscapes and global change. New frontiers in management, conservation and restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference

This volume contains the contributions of numerous participants at the IUFRO Landscape Ecology Working Group International Conference, which took place in Bragança, Portugal, from 21 to 24 of September 2010. The conference was dedicated to the theme Forest Landscapes and Global Change - New Frontiers in Management, Conservation and Restoration. The 128 papers included in this book follow the structure and topics of the conference. Sections 1 to 8 include papers relative to presentations in 18 thematic oral and two poster sessions. Section 9 is devoted to a wide-range of landscape ecology fields covered in the 12 symposia of the conference. The Proceedings of the IUFRO Landscape Ecology Working Group International Conference register the growth of scientific interest in forest landscape patterns and processes, and the recognition of the role of landscape ecology in the advancement of science and management, particularly within the context of emerging physical, social and political drivers of change, which influence forest systems and the services they provide. We believe that these papers, together with the presentations and debate which took place during the IUFRO Landscape Ecology Working Group International Conference – Bragança 2010, will definitively contribute to the advancement of landscape ecology and science in general. For their additional effort and commitment, we thank all the participants in the conference for leaving this record of their work, thoughts and science

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

Analysis and design of multifunctional agricultural landscapes : a graph theoretic approach

This thesis deals with the development of quantitative methodologies for the evaluation of landscape functions and their interactions in multifunctional agricultural landscapes. It focuses on the spatial coherence of hedgerow networks for ecological functions and landscape character for perception of landscape identity, and on their integration in a multifunctional and multiscale trade-off analysis. Graph theory provided the basis for new methodologies that are applied in this research

Computational imaging and automated identification for aqueous environments

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of longline operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008